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MKT317 EXAM 2 READING Q-S QUESTIONS WITH COMPLETE ANSWERS
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MKT317 EXAM 2 READING Q-S QUESTIONS WITH COMPLETE ANSWERS
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MKT317 EXAM 2 READING Q'SQUESTIONS WITH COMPLETE
ANSWERS
Suppose ccwe cccreated cca ccmultiple cclinear ccregression ccmodel ccusing ccthe ccvariable
cclocation cc(the cclocation ccis cceither ccNORTH ccor ccSOUTH, ccand ccthey ccare ccincluded
ccin ccthe ccmodel ccin ccdummy ccvariable ccformat).
The ccmodel ccwe ccobtained ccis
Estimated ccaverage ccselling ccprice cc(in ccthousands ccof ccdollars) cc= cc13.9 cc+
cc0.07(size, ccin ccsqft) cc+ cc2(SOUTH)
Use ccthe ccmodel ccto cccompute ccthe ccestimated ccaverage ccselling ccprice ccof cca cc2000
ccsquare ccfoot cchouse ccthat ccis ccin ccthe ccSOUTH cclocation. cc- ccAnswer cc155.9
ccthousand ccdollars
Suppose ccwe cccreated cca ccmultiple cclinear ccregression ccmodel ccusing ccthe ccvariable
cclocation cc(the cclocation ccis cceither ccNORTH ccor ccSOUTH, ccand ccthey ccare ccincluded
ccin ccthe ccmodel ccin ccdummy ccvariable ccformat).
The ccmodel ccwe ccobtained ccis
Estimated ccaverage ccselling ccprice cc(in ccthousands ccof ccdollars) cc= cc13.9 cc+
cc0.07(size, ccin ccsqft) cc+ cc2(SOUTH)
Use ccthe ccmodel ccto cccompute ccthe ccestimated ccaverage ccselling ccprice ccof cca cc2000
ccsquare ccfoot cchouse ccthat ccis ccin ccthe ccNORTH cclocation. cc- ccAnswer cc153.9
ccthousand ccdollars
Using ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired cclinear
ccregression ccmodel?
Model cc<- cclm(as.factor(Spending) cc~ ccas.factor(FICO) cc+ ccas.factor(Years) cc+
ccas.factor(AgeGroup) cc+ ccas.factor(Segment), ccdata=DF)
summary(Model) cc- ccAnswer ccNo cc- ccthis ccwill ccnot cccreate ccthe ccdesired ccmodel.
Using ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired cclinear
ccregression ccmodel?
Model cc<- cclm(Spending cc~ ccFICO cc+ ccYears cc+ ccAgeGroup cc+ ccSegment,
ccdata=DF)
summary(Model) cc- ccAnswer ccNo cc- ccthis ccwill ccnot cccreate ccthe ccdesired ccmodel.
,Using ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired cclinear
ccregression ccmodel?
Model cc<- cclm(Spending cc~ ccFICO cc+ ccYears cc+ ccas.factor(AgeGroup) cc+
ccas.factor(Segment), ccdata=DF)
summary(Model) cc- ccAnswer ccYes cc- ccthis ccwill cccreate ccthe ccdesired ccmodel.
Using ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired cclinear
ccregression ccmodel?
Model cc<- cclm(as.factor(Spending) cc~ ccFICO cc+ ccYears cc+ ccas.factor(AgeGroup) cc+
ccas.factor(Segment), ccdata=DF)
summary(Model) cc- ccAnswer ccNo cc- ccthis ccwill ccnot cccreate ccthe ccdesired ccmodel.
7. ccUsing ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired
cclinear ccregression ccmodel?
Model cc<- cclm(Spending cc~ ccFICO cc+ ccYears cc+ ccAgeGroup cc+ ccas.factor(Segment),
ccdata=DF)
summary(Model) cc- ccAnswer ccYes cc- ccthis ccwill cccreate ccthe ccdesired ccmodel.
8. ccUsing ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired
cclinear ccregression ccmodel?
Model cc<- cclm(Spending cc~ ccas.factor(FICO) cc+ ccYears cc+ ccas.factor(AgeGroup) cc+
ccas.factor(Segment), ccdata=DF)
summary(Model) cc- ccAnswer ccNo cc- ccthis ccwill ccnot cccreate ccthe ccdesired ccmodel.
9. ccUsing ccthe ccdata ccabove, ccwill ccthe cccommands ccbelow cccreate ccthe ccdesired
cclinear ccregression ccmodel?
Model cc<- cclm(Spending cc~ ccFICO cc+ ccYears cc+ ccas.factor(AgeGroup) cc+ ccSegment,
ccdata=DF)
summary(Model) cc- ccAnswer ccNo cc- ccthis ccwill ccnot cccreate ccthe ccdesired ccmodel.
Since ccthe ccaverage ccprice ccof cca cccolor ccJ ccdiamond ccis cchigher ccthan ccthe
ccaverage ccprice ccof cca cccolor ccD ccdiamond, ccit ccmeans ccthat ccthe ccpeople ccare
ccwilling ccto ccpay ccfor ccmore ccdiamonds ccthat ccare cccolor ccJ ccthan cccolor ccD cc- cccolor
ccJ ccis ccthe ccmore ccdesired cccolor. cc- ccAnswer ccfalse
Since cccolor ccD ccdiamonds ccappear ccto ccbe ccmore ccexpensive cconce ccwe cchave
ccaccounted ccfor ccthe ccsize, ccbut ccthe ccaverage ccprice ccof cca cccolor ccJ ccdiamond ccis
cchighest ccoverall, ccit's cclikely ccthat ccamong ccdiamonds ccincluded ccin ccthe ccdata ccset,
ccthere ccare ccmany cclarge cc(and cctherefore ccexpensive) cccolor ccJ ccdiamonds, ccand
ccmany ccsmall cc(and cctherefore ccless ccexpensive) cccolor ccD ccdiamonds. cc- ccAnswer
cctrue
In ccthe ccfirst ccscatter ccplot, ccthe ccprice ccaxis cclabels ccthe ccvalues cc300, cc1000,
cc3000, cc10000, cc30000. ccIs ccprice ccbeing ccgraphed ccusing ccthe cclinear ccscale ccor
ccthe cclog ccscale? cc- ccAnswer cclog ccscale
, Overall cc(not ccaccounting ccfor ccagency), ccthe ccpredicted ccprice ccof cca cc2000 ccsquare
ccfoot cchouse ccis cc- ccAnswer ccThe ccmodel cccreated ccin ccthis ccsection, ccwhich ccwas
cccreated ccusing ccthe ccR cccommands ccModel cc<- cclm(PRICE cc~ ccSIZE cc+
ccas.factor(AGENCY), ccdata=HOUSEDATA), ccdoes ccnot ccgive ccus ccenough
ccinformation ccto ccanswer ccthis ccquestion.
explanation cc: ccThe ccmodel ccthat ccwas ccgiven ccaccounts ccfor ccAgency, ccand ccall
ccinterpretations ccand cccomputations ccwill ccaccount ccfor ccAgency. ccIf ccwe ccdon't ccwant
ccto ccaccount ccfor ccagency cc(and cconly ccsquare ccfootage), ccthen ccwe ccneed ccto
ccmake cca ccnew ccmodel ccwhere ccthe cconly ccx-variable ccis ccSIZE; ccthe cccurrent
ccmodel ccdoes ccnot ccprovide ccenough ccinformation ccto ccmake ccthe ccdesired
cccalculation.
True ccor ccFalse: ccThe ccaverage ccselling ccprice ccfor cchomes ccsold ccby ccAgency cc2 ccis
ccabout cc18.72 ccthousand ccdollars ccless ccthan ccthe ccaverage ccselling ccprice ccof
cchomes ccsold ccby ccAgency cc1. cc- ccAnswer ccfalse,
ccThe cc18.72 cccame ccfrom cca ccmodel ccthat ccincludes ccSIZE, ccso ccthe ccinterpretation
ccmust ccinclude cc"among cchomes ccof ccthe ccsame ccsize"
True ccor ccFalse: ccWhen cccomparing cchomes ccof ccthe ccsame ccsize, ccif cca cchouse ccis
ccsold ccby ccAgency cc4, ccthe ccaverage ccprice ccdecreases ccabout cc17.39 ccthousand
ccdollars. cc- ccAnswer ccfalse
How ccdo ccwe ccknow ccthat ccthis ccmodel ccwas ccconstructed ccincorrectly? cc- ccAnswer
ccbThis ccmodel ccis ccincorrect ccbecause ccthe ccoutput ccdoes ccnot ccinclude ccdummy
ccvariables; ccthe ccclassmate ccshould cchave ccused ccas.factor(AGENCY).
Create ccthe ccmultiple cclinear ccregression ccmodel ccwhere ccthe ccindependent ccvariables
ccare ccSIZE, ccTRAIN.DIST, ccand ccthree ccof ccthe ccfour ccdummy ccvariables ccassociated
ccwith ccAGENCY. ccWhat ccpercentage ccof ccthe ccvariation ccin ccprice ccis ccexplained ccby
ccthe ccvariables ccSIZE, ccTRAIN.DIST, ccand ccAGENCY? ccIn ccother ccwords, ccwhat ccis
ccthe ccMultiple ccR-squared ccof ccthis ccmodel cc(as ccshown ccat ccthe ccbottom ccof ccthe
ccsummary ccoutput)? cc- ccAnswer cc0.9460
When cccomparing cchomes ccof ccthe ccsame ccsize ccand ccsame ccdistance ccfrom ccthe
cctrain, ccwhat cccan ccwe ccsay ccabout ccthe ccdifference ccin ccaverage ccprices ccbetween
ccthe ccNorth ccand ccthe ccSouth? cc- ccAnswer ccWhen cccomparing cchomes ccof ccthe
ccsame ccsize ccand ccsame ccdistance ccfrom ccthe cctrain, ccthere ccis ccnot cca ccsignificant
ccdifference ccin ccaverage ccprice ccbetween cchomes ccin ccthe ccNorth ccand ccthe ccSouth.
Above, ccwe cccreated ccthe ccmodel: ccLOCATIONModel cc<- cclm(PRICE cc~ ccLOCATION,
ccdata=HOUSEDATA)
What cccan ccwe ccinterpret ccfrom ccthe ccoutput ccfrom ccthis ccmodel? cc(ignore ccoutput
ccfrom ccall ccprevious ccmodels) cc- ccAnswer ccWhen ccnot ccaccounting ccfor ccany ccother
ccpiece ccof ccinformation ccabout cchomes, ccthe ccaverage ccselling ccprice ccof cchouses ccin
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